title: Impact of comorbidities on stroke rehabilitation outcomes: does the method matter?
abstract: OBJECTIVE
  To examine the impact of comorbidities in predicting stroke rehabilitation outcomes and to examine differences among 3 commonly used comorbidity measures--the Charlson Index, adjusted clinical groups (ACGs), and diagnosis cost groups (DCGs)--in how well they predict these outcomes.
  
  
  METHODS
  Inception cohort of patients followed for 6 months.
  
  
  METHODS
  Department of Veterans Affairs (VA) hospitals.
  
  
  METHODS
  A total of 2402 patients beginning stroke rehabilitation at a VA facility in 2001 and included in the Integrated Stroke Outcomes Database.
  
  
  METHODS
  Not applicable.
  
  
  METHODS
  Three outcomes were evaluated: 6-month mortality, 6-month rehospitalization, and change in FIM score.
  
  
  RESULTS
  During 6 months of follow-up, 27.6% of patients were rehospitalized and 8.6% died. The mean FIM score increased an average of 20 points during rehabilitation. Addition of comorbidities to the age and sex models improved their performance in predicting these outcomes based on changes in c statistics for logistic and R(2) values for linear regression models. While ACG and DCG models performed similarly, the best models, based on DCGs, had a c statistic of .74 for 6-month mortality and .63 for 6-month rehospitalization, and an R(2) of .111 for change in FIM score.
  
  
  CONCLUSIONS
  Comorbidities are important predictors of stroke rehabilitation outcomes. How they are classified has important implications for models that may be used in assessing quality of care.
authors.affiliation: Center for Health Quality, Outcomes and Economic Research, Bedford VA Hospital, Bedford, MA 01730, USA. dberlow@bu.edu
authors.forename: Dan R
authors.initials: DR
authors.lastname: Berlowitz
authors.forename: Helen
authors.initials: H
authors.lastname: Hoenig
authors.forename: Diane C
authors.initials: DC
authors.lastname: Cowper
authors.forename: Pamela W
authors.initials: PW
authors.lastname: Duncan
authors.forename: W Bruce
authors.initials: WB
authors.lastname: Vogel
mesh_terms: D000368:Aged; D015897:Comorbidity; D005260:Female; D006801:Humans; D016014:Linear Models; D008297:Male; D010359:Patient Readmission / Q000706:statistics & numerical data; D020521:Stroke / Q000401:mortality; D000071939:Stroke Rehabilitation; D016896:Treatment Outcome; D014481:United States; D014728:Veterans